Electronic Journal of Statistics
- Electron. J. Statist.
- Volume 10, Number 2 (2016), 2000-2038.
Change-point detection in panel data via double CUSUM statistic
In this paper, we consider the problem of (multiple) change-point detection in panel data. We propose the double CUSUM statistic which utilises the cross-sectional change-point structure by examining the cumulative sums of ordered CUSUMs at each point. The efficiency of the proposed change-point test is studied, which is reflected on the rate at which the cross-sectional size of a change is permitted to converge to zero while it is still detectable. Also, the consistency of the proposed change-point detection procedure based on the binary segmentation algorithm, is established in terms of both the total number and locations (in time) of the estimated change-points. Motivated by the representation properties of the Generalised Dynamic Factor Model, we propose a bootstrap procedure for test criterion selection, which accounts for both cross-sectional and within-series correlations in high-dimensional data. The empirical performance of the double CUSUM statistics, equipped with the proposed bootstrap scheme, is investigated in a comparative simulation study with the state-of-the-art. As an application, we analyse the log returns of S&P 100 component stock prices over a period of one year.
Electron. J. Statist., Volume 10, Number 2 (2016), 2000-2038.
Received: November 2015
First available in Project Euclid: 18 July 2016
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Cho, Haeran. Change-point detection in panel data via double CUSUM statistic. Electron. J. Statist. 10 (2016), no. 2, 2000--2038. doi:10.1214/16-EJS1155. https://projecteuclid.org/euclid.ejs/1468849969
- Supplement to “Change-point detection in panel data via double CUSUM statistic”. We provide the detailed description of the Local Bootstrap and proof of some auxillary results. In addition, the tables and plots summarising the outcome of the simulation studies conducted in Section 5 are presented.